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Tavlama benzetim algoritmasıyla portföy optimizasyonu: Borsa İstanbul uygulaması

Yıl 2024, Cilt: 10 Sayı: 1, 1 - 15, 28.02.2024
https://doi.org/10.30855/gjeb.2024.10.1.001

Öz

Finans alanının önemli konularından Markowitz’in kısıtlı ortalama-varyans modelinde, portföye dahil edilecek varlık sayısı sınırlandırılır. Kuadratik ve tamsayılı programlama problem sınıfına ait genelleştirilmiş bu problemin, boyut sayısının artmasıyla çözümünün standart yöntemlerle elde edilmesi zordur. Bu çalışmada yerel arama tabanlı meta-sezgisel yöntemlerden olan tavlama benzetim (TB) algoritması tercih edilmiş, geliştirilen TB algoritması Hang-Seng benchmark veri setine uygulanmış, sonuçlar öncü çalışmalarla kıyaslanmıştır. Markowitz kısıtlı ortalama-varyans modeline dayanarak elde edilen kısıtsız etkin sınıra yaklaşabilmek için, düşük risk düzeyinde varlık sayısının daha fazla, yüksek risk seviyesinde varlık sayısının daha az olması gerektiği sonucuna ulaşılmıştır.

Kaynakça

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  • Adıgüzel Mercangöz, B. (2019). Parçacık sürü optimizasyonu ile portföy optimizasyonu: Borsa İstanbul ulaştırma sektörü hisseleri üzerine bir uygulama. Journal of Yasar University, 14 (Special Issue), 126-136.
  • Akyer, H., Kalaycı, C. B. and Aygören, H. (2018), Ortalama varyans portföy optimizasyonu için parçacık sürü optimizasyonu algoritması: Bir Borsa İstanbul uygulaması. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi, 24(1), 124-129. Doi: http://doi.org/ 10.5505/pajes.2017.91145
  • Baykasoğlu, A., Yunusoglu, M. G. and Özsoydan F. B. (2015). A GRASP based solution approach to solve cardinality constrained portfolio optimization problems. Computers and Industrial Engineering, 90(2015), 339-351. Doi: https://doi.org/10.1016/j.cie.2015.10.009.
  • Bermúdez, J. D., Segura, J. V. and Vercher, E. (2012), A multi-objective genetic algorithm for cardinality constrained fuzzy portfolio selection, Fuzzy Sets and Systems, 188(1), 16-26. Doi:https://doi.org/10.1016/j.fss.2011.05.013
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The portfolio optimization with simulated annealing algorithm: An application of Borsa Istanbul

Yıl 2024, Cilt: 10 Sayı: 1, 1 - 15, 28.02.2024
https://doi.org/10.30855/gjeb.2024.10.1.001

Öz

One of the key concepts in finance is Markowitz’s constrained mean-variance model, the number of assets to be included in the portfolio is restricted. The solution of this generalized problem, which belongs to the quadratic and integer programming problem class, as the number of dimensions increases, is difficult to obtain with standard methods. In this study, the simulated annealing (SA) algorithm, which is one of the local search-based meta-heuristic methods, was preferred. The developed SA algorithm was applied to the Hang-Seng benchmark data set, and the results were compared with pioneering studies. According to the experimental results, upon the performance of the algorithm was found to be sufficient, the SA algorithm was applied for the Borsa Istanbul 30 index. The results of the experiments based on the Markowitz mean-variance model demonstrate that, while more assets must be maintained at lower risk levels to converge an unconstrained efficient frontier and the number of assets needed to do so decreases as risk rises

Kaynakça

  • Abuelfadl, M. (2017). Quantum particle swarm optimization for short-team portfolios, Journal of Accounting and Finance. 17(8), 121-137.
  • Ackora-Prah, J., Gyamerah, S. A., Andam, P. S., and Gyamfi, D. (2014), Pattern search for portfolio selection, Applied Mathematical Science, 8(143), 7137-7147. Doi:http://dx.doi.org/10.12988/ams.2014.46425
  • Adıgüzel Mercangöz, B. (2019). Parçacık sürü optimizasyonu ile portföy optimizasyonu: Borsa İstanbul ulaştırma sektörü hisseleri üzerine bir uygulama. Journal of Yasar University, 14 (Special Issue), 126-136.
  • Akyer, H., Kalaycı, C. B. and Aygören, H. (2018), Ortalama varyans portföy optimizasyonu için parçacık sürü optimizasyonu algoritması: Bir Borsa İstanbul uygulaması. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi, 24(1), 124-129. Doi: http://doi.org/ 10.5505/pajes.2017.91145
  • Baykasoğlu, A., Yunusoglu, M. G. and Özsoydan F. B. (2015). A GRASP based solution approach to solve cardinality constrained portfolio optimization problems. Computers and Industrial Engineering, 90(2015), 339-351. Doi: https://doi.org/10.1016/j.cie.2015.10.009.
  • Bermúdez, J. D., Segura, J. V. and Vercher, E. (2012), A multi-objective genetic algorithm for cardinality constrained fuzzy portfolio selection, Fuzzy Sets and Systems, 188(1), 16-26. Doi:https://doi.org/10.1016/j.fss.2011.05.013
  • Çelenli, A. Z., Eğrioğlu, E. and Çorba, B. Ş. (2015), İMKB 30 indeksini oluşturan hisse senetleri için parçacık sürü optimizasyonu yöntemlerine dayalı portföy optimizasyonu, Dogus Univesity Journal, 6(1), 25-33.
  • Cerny, V. (1985). Thermo dynamical approach to the traveling salesman problem: an efficient simulation algorithm, J. Optimisation Theory Appl, 45(1), 41-51. Doi: https://doi.org/10.1007/BF00940812.
  • Cesarone, F., Scozzari, A. and Tardella, F. (2013), A new method for mean-variance portfolio optimization with cardinality constraints, Annals of Operations Research, 205(1), 213–234. Doi: http://doi:10.1007/s10479-012-1165-7.
  • Chang, T. J, Yang, S. C. and Chang, K. J. (2009), Portfolio optimization problems in different risk measures using genetic algorithm, Expert Systems with Applications, 36(7), 10529-10537. Doi:https://doi.org/10.1016/j.eswa.2009.02.062
  • Chang, T.J, Meade, N., Beasley J. E. and Sharaiha, Y.M. (2000), Heuristics for cardinality constrained portfolio optimisation, Computers and Operations Research, 27, 1271-1302.
  • Chen, B., Lin, Y., Zeng, W., Xu, H. and Zhang, D. (2017), The mean-variance cardinality constrained portfolio optimization problem using a local search-based multi-objective evolutionary algorithm. Applied Intelligence, 47(2), 505-525. Doi:10.1007/s10489-017-0898-z
  • Chen, Y., Mabu, S. and Hirasawa, K. (2011), Genetic relation algorithm with guided mutation for the large-scale portfolio optimization, Expert Systems with Applications, 38(4), 3353-3363. Doi: https://doi.org/10.1016/j.eswa.2010.08.120
  • Corazza, M, Fasano, G. and Gusso, R. (2013), Particle swarm optimization with non-smooth penalty reformulation, for a complex portfolio selection problem, Applied Mathematics and Computation, 224, 611-624. Doi:https://doi.org/10.1016/j.amc.2013.07.091
  • Coutino-Gomez, C.A., Torres-Jimenez J. and Villarreal-Antelo B.M. (2003), Heuristic methods for portfolio selection at the Mexican stock exchange In J. Liu, Y. Cheung and H. Yin (Eds.), Intelligent Data Engineering and Automated Learning, Berlin, Heidelberg: Springer.
  • Crama, Y. and Schyns, M. (2003), Portfolio selection problems, European Journal of Operational Research, 150(3), 546-571.
  • Cura, T. (2009), Particle swarm optimization approach to portfolio optimization, Nonlinear Analysis: Real World Applications, 10(4), 2396-2406. Doi: https://doi.org/10.1016/j.nonrwa.2008.04.023
  • Delahaye D., Chaimatanan, S. and Mongeau, M. (2018). Simulated annealing: From basics to applications. In M. Gendreau and J. Y. Potvin (Eds.), Handbook of Metaheuristics. International Series in Operations Research and Management Science, 272. Springer. Doi: https://doi.org/10.1007/978-3-319-91086-4_1.
  • Deng, G.F., Lin, W.T. and Lo, C.C. (2012), Markowitz based portfolio selection with cardinality constraints using improved particle swarm optimization. Expert Systems with Applications, 39(4), 4558-4566. Doi: https://doi.org/10.1016/j.eswa.2011.09.129
  • Eshlaghy, T.A., Abdolahi, A., Moghadasi, M. and Maatofi, A. (2011), Using genetic and particle swarm algorithms to select and optimize portfolios of companies admitted to Tehran stock exchange, Research Journal of Internatıonal Studies, 20, 95-105.
  • Fastrich, B. and Winker, P. (2012), Robust portfolio optimization with a hybrid heuristic algorithm, Computational Management Science, 9(1), 63-88. Doi: https://doi.org/ 10.1007/s10287-010-0127-2
  • Fernandez, A. and Gomez, S. (2007), Portfolio selection using neural networks, Computers and Operations Research, 34(4), 1177–1191.
  • García, F., Guijarro, F. and Oliver, J. (2018), Index tracking optimization with cardinality constraint: a performance comparison of genetic algorithms and tabu search heuristics, Neural Computing and Applications, 30(8), 2625-2641.
  • Golmakani, H. R. and Fazel, M. (2011), Constrained portfolio selection using particle swarm optimization, Expert Systems with Applications, 38(7), 8327-8335. Doi: https://doi.org/10.1016/j.eswa.2011.01.020
  • Gorgulho, A., Neves, R. and Horta, N. (2011), Applying a GA kernel on optimizing technical analysis rules for stock picking and portfolio composition, Expert Systems with Applications, 38(11), 14072-14085. Doi: https://doi.org/10.1016/j.eswa.2011.04.216
  • Hsu, C. M. (2014), An integrated portfolio optimisation procedure based on data envelopment analysis, artificial bee colony algorithm and genetic programming, International Journal of Systems Science, 45(12), 2645-2664. Doi: https://doi.org/10.1080/00207721.2013.775388
  • Jadhav, D. and Ramanathan, T.V. (2018), Portfolio optimization based on modified expected shortfall, Studies in Economics and Finance, 36(3), 440-463. Doi: 10.1108/SEF-05-2018-0160.
  • Jalota, H. and Thakur, M. (2018), Genetic algorithm designed for solving portfolio optimization problems subjected to cardinality constraint, International Journal of System Assurance Engineering and Management, 9(1), 294-305. Doi: https://doi.org/10.1007/s13198-017-0574-z
  • Kalaycı, C. B., Ertenlice, O. and Akbay, M. A. (2019), A comprehensive review of deterministic models and applications for mean-variance portfolio optimization, Expert Systems with Applications, 125, 345-368. Doi: https://doi.org/10.1016/j.eswa.2019.02.011
  • Kalaycı, C. B., Ertenlice, O., Akyer and H., Aygören, H. (2017-b), An artificial bee colony algorithm with feasibility enforcement and infeasibility toleration procedures for cardinality constrained portfolio optimization, Expert Systems with Applications, 85, 61–75. Doi: https://doi.org/10.1016/j.eswa.2017.05.018
  • Kalaycı, C. B., Ertenlice, Ö., Akyer, H. and Aygören, H. (2017-a). A review on the current applications of genetic algorithms in mean-variance portfolio optimization. Pamukkale University Journal of Engineering Sciences, 23(4), 470-476. Doi: https://doi.org/10.5505/pajes.2017.37132
  • Kalaycı, C.B., Polat O. and Akbay M.A. (2020), An efficient hybrid metaheuristic algorithm for cardinality constrained portfolio optimization, Swarm and Evolutionary Computation, 54 (2020) 100662. Doi: https://doi.org/10.1016/j.swevo.2020.100662
  • Kamali, S. (2014), Portfolio optimization using particle swarm optimization and genetic algorithm, Journal of Mathematics and Computer Science, 10(2), 85-90.
  • Kirkpatrick, S., Gelatt, C.D. and Vecchi, M.P. (1983). Optimization by simulated annealing. Science, 220(1), 671–680.Doi: https://doi.org/10.1126/science.220.4598.67
  • Kumar C., Doja M.N. and Baig M.A. (2018), A novel framework for portfolio optimization based on modified simulated annealing algorithm using ANN, RBFN, and ABC algorithms, In S. Chakraverty, A. Goel And S. Misra (Eds.) Towards Extensible and Adaptable Methods İn Computing. Singapore: Springer: https://doi.org/10.1007/978-981-13-2348-5_13.
  • Lukovac, V., Pamucar D., Popovic, M. and Dorovic, B. (2017), Portfolio model for analyzing human resources: an approach based on neuro-fuzzy modeling and the simulated annealing algorithm, Expert Systems with Applications 90(1), 318-331. Doi: https://doi.org/10.1016/j.eswa.2017.08.034
  • Maringer, D. and Kellerer, H. (2003), Optimization of cardinality constrained portfolios with a hybrid local search algorithm OR Spectrum, 25(4), 481-495.Doi: https://doi.org/10.1007/s00291-003-0139-1
  • Markowitz, H. (1952), Portfolio selection, Journal of Finance, (1), 77-91.
  • Meghwani, S. S. and Thakur, M. (2018), Multi - objective heuristic algorithms for practical portfolio optimization and rebalancing with transaction, Cost Applied Soft Computing, 67, 865-894 https://doi.org/10.1016/j.asoc.2017.09.025.
  • Metaxiotis, K., and Liagkouras, K. (2012). Multiobjective evolutionary algorithms for portfolio management: A comprehensive literature review. Expert Systems With Applications, 39(14), 11685-11698. Doi: https://doi.org/10.1016/j.eswa.2012.04.053
  • Metropolis, N., Rosenbluth A.W., Rosenbluth, M.N., Teller, A.H. and Teller E., (1953), Equation of State Calculations by Fast Computing Machines, J. Chem. Phys. 21(6), 1087-1092.
  • Moradi, N., Kayvanfar, V. and Rafiee, M. (2021), An efficient population-based simulated annealing algorithm for 0–1 knapsack problem, Engineering with Computers. https://doi.org/10.1007/s00366-020-01240-3.
  • Moral-Escudero, R., Ruiz-Torrubiano, R. and Suarez, A. (2006), Selection of optimal investment portfolios with cardinality constraints, IEEE International Conference on Evolutionary Computation,Doi: http://doi:10.1109/CEC.2006.1688603.
  • Mozafari, M., Jolai, F. and Tafazzoli, S. (2011), A new IPSO-SA approach for cardinality constrained portfolio optimization, International Journal of Industrial Engineering Computations, 2(2), 249-262. Doi: https://doi.org/10.5267/j.ijiec.2011.01.004
  • Ni, Q., Yin, X., Tian, K. and Zhai, Y. (2017), Particle swarm optimization with dynamic random population topology strategies for a generalized portfolio selection problem, Natural Computing, 16(1), 31-44. Doi: https://doi.org /10.1007/s11047-016-9541-x
  • Özdemir, M. (2011), Genetik algoritma kullanarak portföy seçimi, İktisat İşletme ve Finans, 26(229), 43-66.
  • Pandari, A. R., Azar, A. and Shavazi, A. R. (2012), Genetic algorithms for portfolio selection problems with non-linear objectives, African Journal of Business Management, 6(20), 6209-6216. Doi: https://doi.org /10.5897/AJBM11.2876
  • Qodsi, S., Tehrani, R. and Bashiri, M. (2015), Portfolio optimization with simulated annealing algorithm, Financial Research Journal, 17(1), 141-158. doi: 10.22059/jfr.2015.52036.
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  • Sasaki, M., Laamrani, A., Yamashiro, M., Alehegn, C. and Kamoyedji, A. (2018), Portfolio optimization by fuzzy interactive genetic algorithm, Journal of Advanced Management Science, 6(3).
  • Schaerf, A. (2002), Local search techniques for constrained portfolio selection problems, Computational Economics, 20(3), 177-190.
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  • Wang, J., Chen, W.N., Zhang, J. and Lin Y. (2015), A dimension-decreasing particle swarm optimization method for portfolio optimization, Annual Conference on Genetic and Evolutionary Computation (GECCO Companion’15), Doi: https://doi.org/10.1145/2739482.2764652.
  • Wong, W. K. (2020), Review on behavioral economics and behavioral finance, Studies in Economics and Finance, 37(4), 1086-7376. Doi: 10.1108/SEF-10-2019-0393.
  • Woodside-Oriakhi, M., Lucas, C. and Beasley, J. E. (2011), Heuristic algorithms for the cardinality constrained efficient frontier, European Journal of Operational Research, 213(3), 538-550. Doi: https://doi.org/10.1016/j.ejor.2011.03.030
  • Yakut, E. and Çankal, A. (2016), Çok amaçlı genetik algoritma ve hedef programlama metotlarini kullanarak hisse senedi portföy optimizasyonu: Bist-30’da bir uygulama, Business and Economics Research Journal, 7(2), 43. Doi: https://doi.org 10.20409/berj.2016217495
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  • Zhu, H., Wang, Y., Wang, K. and Chen, Y. (2011), Particle swarm optimization (PSO) for the constrained portfolio optimization problem, Expert Systems with Applications, 38(8), 10161-10169. Doi: https://doi.org/10.1016/j.eswa.2011.02.075
Toplam 64 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Yöneylem, Finans
Bölüm Makaleler
Yazarlar

Seyyide Doğan 0000-0001-7835-7905

Müge Sağlam Bezgin 0000-0001-8674-2707

Emine Karaçayır 0000-0003-0512-9084

Erken Görünüm Tarihi 28 Şubat 2024
Yayımlanma Tarihi 28 Şubat 2024
Yayımlandığı Sayı Yıl 2024 Cilt: 10 Sayı: 1

Kaynak Göster

APA Doğan, S., Sağlam Bezgin, M., & Karaçayır, E. (2024). The portfolio optimization with simulated annealing algorithm: An application of Borsa Istanbul. Gazi İktisat Ve İşletme Dergisi, 10(1), 1-15. https://doi.org/10.30855/gjeb.2024.10.1.001
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